In [1]:
suppressMessages(library(Signac))
suppressMessages(library(Seurat))
suppressMessages(library(tidyverse))
suppressMessages(library(data.table))
suppressMessages(library(RColorBrewer))
suppressMessages(library(readxl))
library(harmony)
library(SeuratWrappers)
Loading required package: Rcpp

In [2]:
panc <- readRDS('/diskmnt/Projects/SenNet_analysis/Main.analysis/merged/merge_SBR_RNA_1/17_Mouse_Merged_normalized_SBR_2_10_26_weeks.rds')
In [3]:
setwd('/diskmnt/Projects/SenNet_analysis/Main.analysis/merged/merge_SBR_RNA_1/upd.cell.type/')
In [6]:
DimPlot(panc, group.by = 'seurat_clusters', label = TRUE, raster = T)
ggsave('Dimplot_clusters.pdf', width = 7, height = 5)
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`

In [34]:
options(repr.plot.width=8, repr.plot.height=7)
DimPlot(panc, group.by = 'Timepoint.weeks', label = TRUE, raster = T)
DimPlot(panc, group.by = 'Cohort', label = TRUE, raster = T)
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`

Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`

In [35]:
panc$Mouse_ID %>% table()
.
 7248  7249  7251  7254  7259  7260  7352  7353  7354  7357  7361  7434  7435 
 6660  8171  5120  5231  2861  6391 12937 16472 18134 15188  9480 11497  7887 
 7448  7450  7452  7548 
 7615  6873  9773 12322 
In [10]:
options(repr.plot.width=8, repr.plot.height=7)

panc$predicted_doublet[is.na(panc$predicted_doublet)] <- NA
DimPlot(object = panc, group.by = 'predicted_doublet',cols = c('black', 'yellow'), raster = T,
        label=FALSE,label.size=6)
ggsave( "Dimplot_predicted_doublets.pdf", 
       height=6,width=8,useDingbats=FALSE,limitsize = FALSE)


options(repr.plot.width=14, repr.plot.height=7)
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`

In [11]:
degs <- fread('/diskmnt/Projects/Users/allakarpova/Data/markers/Liver_mouse_markers.txt')

genes2plot <- degs$`Gene symbol` %>% unique()
p <- DotPlot(object = panc, group.by = 'seurat_clusters', features = genes2plot, col.min = 0, dot.min = 0.01, assay = 'RNA' )
p$data <- merge(p$data, degs[,c(1,5)], by.x = 'features.plot', by.y = 'Gene symbol', all.x=T)
p <- p  + RotatedAxis()
p <- p + facet_wrap(~ Cell.type , scales = "free",  drop = T, ncol = 3)
p <- p + theme(panel.spacing = unit(0, "lines"),
               strip.background = element_blank(),
               panel.border = element_rect(colour = "black"),
               panel.grid.major = element_line(colour = "grey80"),
               strip.text.x = element_text(angle = 00, vjust = 0.5, hjust = 0.5, size = 14),
               axis.text.x = element_text(size = 15, face = "bold", angle = 90, hjust = 1, vjust = 0.5),
               strip.placement = "outside")
p <- p + scale_color_viridis_c("viridis", direction = 1) +
    scale_size_area(limits=c(0,40), oob=scales::squish)
ggsave('Dotplot_markers.pdf', width = 30, height =30, useDingbats = F)


options(repr.plot.width=14, repr.plot.height=24)
p
Warning message in FetchData.Seurat(object = object, vars = features, cells = cells):
“The following requested variables were not found: Gstp-ps, Mug-ps1, mt-Rnr1, mt-Rnr2, Sdpr, Cyr61”
Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.
Scale for size is already present.
Adding another scale for size, which will replace the existing scale.
Warning message:
“Removed 1424 rows containing missing values or values outside the scale range (`geom_point()`).”
Warning message:
“Removed 1424 rows containing missing values or values outside the scale range (`geom_point()`).”
In [12]:
mouse_human_genes <- read.csv("http://www.informatics.jax.org/downloads/reports/HOM_MouseHumanSequence.rpt",sep="\t")

# separate human and mouse 
mouse <- split.data.frame(mouse_human_genes,mouse_human_genes$Common.Organism.Name)[[2]]
human <- split.data.frame(mouse_human_genes,mouse_human_genes$Common.Organism.Name)[[1]]

# remove some columns
mouse <- mouse[,c(1,4)]
human <- human[,c(1,4)]

mh_data <- merge.data.frame(mouse,human,by = "DB.Class.Key",all.y = TRUE, suffixes = c('.mouse', '.human')) 
head(mh_data)

myeloid.genes <- fread('/diskmnt/Projects/Users/allakarpova/Data/markers/Cell_state_markers_v10172022.txt', data.table = F, header = T)


myeloid.genes <- myeloid.genes %>% left_join(mh_data[,-1], by=c('Gene'='Symbol.human'))
genes2plot <- myeloid.genes$Symbol.mouse %>% unique()
p <- DotPlot(object = panc, group.by = 'seurat_clusters', features = genes2plot, col.min = 0, dot.min = 0.01, assay = 'RNA', cluster.idents = TRUE)

p$data <- merge(p$data, myeloid.genes[c(1:3, 6)], by.x = 'features.plot', by.y = 'Symbol.mouse', all.x=T)
p <- p  + RotatedAxis()
p <- p + facet_wrap(~Gene_set_group + Gene_set , scales = "free",  drop = T, ncol = 7)
p <- p + theme(panel.spacing = unit(0, "lines"),
               strip.background = element_blank(),
               panel.border = element_rect(colour = "black"),
               panel.grid.major = element_line(colour = "grey80"),
               strip.text.x = element_text(angle = 00, vjust = 0.5, hjust = 0.5, size = 14),
               axis.text.x = element_text(size = 15, face = "bold", angle = 90, hjust = 1, vjust = 0.5),
               strip.placement = "outside")
p <- p + scale_color_viridis_c("viridis", direction = 1) +
    scale_size_area(limits=c(0,40), oob=scales::squish)

ggsave(paste0( "Dotplot_marker_human_gene_expression_RNA.pdf"),plot = p, 
       height=180,width=50,useDingbats=FALSE,limitsize = FALSE)
A data.frame: 6 × 3
DB.Class.KeySymbol.mouseSymbol.human
<int><chr><chr>
148503904Aldh1l1ALDH1L1
248503905Sry SRY
348503906Fry FRY
448503907Rpe65 RPE65
548503908Ywhae YWHAE
648503909Camkv CAMKV
Warning message in left_join(., mh_data[, -1], by = c(Gene = "Symbol.human")):
“Each row in `x` is expected to match at most 1 row in `y`.
ℹ Row 7 of `x` matches multiple rows.
ℹ If multiple matches are expected, set `multiple = "all"` to silence this warning.”
Warning message in FetchData.Seurat(object = object, vars = features, cells = cells):
“The following requested variables were not found (10 out of 29 shown): NA, Mrgpra7, Mrgpra5, H2-Ea, Vpreb1b, Vpreb1a, Ighg2a, Ighg, Siglecl2, Cd200r1l”
Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.
Scale for size is already present.
Adding another scale for size, which will replace the existing scale.
Warning message:
“Removed 41776 rows containing missing values or values outside the scale range (`geom_point()`).”
In [13]:
degs <- read_excel('/diskmnt/Projects/Users/allakarpova/Data/markers/41467_2018_6318_MOESM5_ESM_formatted.xlsx', 
                   col_names = T)
degs <- degs %>% left_join(mh_data[,-1], by=c('Gene'='Symbol.human'))
degs
genes2plot <- degs$Symbol.mouse %>% unique()
p <- DotPlot(object = panc, group.by = 'seurat_clusters', features = genes2plot, col.min = 0, dot.min = 0.01, assay = 'RNA', cluster.idents = TRUE)
p$data <- merge(p$data, degs[c(4,3)], by.x = 'features.plot', by.y = 'Symbol.mouse', all.x=T)
p <- p  + RotatedAxis()
p <- p + facet_wrap(~ Cell_type , scales = "free",  drop = T, ncol = 3)
p <- p + theme(panel.spacing = unit(0, "lines"),
               strip.background = element_blank(),
               panel.border = element_rect(colour = "black"),
               panel.grid.major = element_line(colour = "grey80"),
               strip.text.x = element_text(angle = 00, vjust = 0.5, hjust = 0.5, size = 14),
               axis.text.x = element_text(size = 15, face = "bold", angle = 90, hjust = 1, vjust = 0.5),
               strip.placement = "outside")
p <- p + scale_color_viridis_c("viridis", direction = 1) +
    scale_size_area(limits=c(0,40), oob=scales::squish)
ggsave(paste0( "Dotplot_20human_liver_paper_markers_gene_expression_RNA.pdf"),
       plot = p, height=70,width=20,useDingbats=FALSE,limitsize = FALSE)
Warning message in left_join(., mh_data[, -1], by = c(Gene = "Symbol.human")):
“Each row in `x` is expected to match at most 1 row in `y`.
ℹ Row 14 of `x` matches multiple rows.
ℹ If multiple matches are expected, set `multiple = "all"` to silence this warning.”
A tibble: 536 × 4
GeneClusterCell_typeSymbol.mouse
<chr><chr><chr><chr>
HSD11B1 Cluster1Hep1 Hsd11b1
APOM Cluster1Hep1 Apom
PON3 Cluster1Hep1 Pon3
TTC36 Cluster1Hep1 Ttc36
BCHE Cluster1Hep1 Bche
F10 Cluster1Hep1 F10
APOC4-APOC2Cluster1Hep1 NA
GOLT1A Cluster1Hep1 Golt1a
ADH1A Cluster1Hep1 Adh1
AQP9 Cluster1Hep1 Aqp9
G6PC Cluster1Hep1 NA
PROX1 Cluster1Hep1 Prox1
RCAN1 Cluster1Hep1 Rcan1
HAMP Cluster1Hep1 Hamp2
HAMP Cluster1Hep1 Hamp
GPD1 Cluster1Hep1 Gpd1
CTH Cluster1Hep1 Cth
TKFC Cluster1Hep1 Tkfc
G0S2 Cluster1Hep1 G0s2
GHR Cluster1Hep1 Ghr
ABCC2 Cluster1Hep1 Abcc2
CD2 Cluster2a/b T-cellsCd2
TRAC Cluster2a/b T-cellsTrac
CD3D Cluster2a/b T-cellsCd3d
CCL5 Cluster2a/b T-cellsCcl5
TRBC2 Cluster2a/b T-cellsTrbc1
TRBC2 Cluster2a/b T-cellsTrbc2
GZMA Cluster2a/b T-cellsGzma
CD3E Cluster2a/b T-cellsCd3e
IL7R Cluster2a/b T-cellsIl7r
⋮⋮⋮⋮
PRDX2 Cluster19Erythroid cells Prdx2
GABARAPL2Cluster19Erythroid cells Gabarapl2
RAD23A Cluster19Erythroid cells Rad23a
FBXO7 Cluster19Erythroid cells Fbxo7
CA2 Cluster19Erythroid cells Car2
HBD Cluster19Erythroid cells Hbb-bh2
HBD Cluster19Erythroid cells Hbb-bt
HBD Cluster19Erythroid cells Hbb-bs
HBD Cluster19Erythroid cells Hbb-b2
HBD Cluster19Erythroid cells Hbb-b1
ACTA2 Cluster20Hepatic stellate cellsActa2
DCN Cluster20Hepatic stellate cellsDcn
MYL9 Cluster20Hepatic stellate cellsMyl9
COL3A1 Cluster20Hepatic stellate cellsCol3a1
COL1A2 Cluster20Hepatic stellate cellsCol1a2
COL1A1 Cluster20Hepatic stellate cellsCol1a1
OLFML3 Cluster20Hepatic stellate cellsOlfml3
TAGLN Cluster20Hepatic stellate cellsTagln
BGN Cluster20Hepatic stellate cellsBgn
TPM2 Cluster20Hepatic stellate cellsTpm2
COLEC11 Cluster20Hepatic stellate cellsColec11
LUM Cluster20Hepatic stellate cellsLum
NEXN Cluster20Hepatic stellate cellsNexn
COL14A1 Cluster20Hepatic stellate cellsCol14a1
GGT5 Cluster20Hepatic stellate cellsGgt5
IGFBP7 Cluster20Hepatic stellate cellsIgfbp7
ASPN Cluster20Hepatic stellate cellsAspn
COL6A2 Cluster20Hepatic stellate cellsCol6a2
COL4A2 Cluster20Hepatic stellate cellsCol4a2
THBS2 Cluster20Hepatic stellate cellsThbs2
Warning message in FetchData.Seurat(object = object, vars = features, cells = cells):
“The following requested variables were not found (10 out of 21 shown): NA, Golt1a, Lyz3, H2-Ea, S100a11-ps, Cstdc3, Tmsb10b, Clec7a, Idi1-ps1, Ighg2a”
Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.
Scale for size is already present.
Adding another scale for size, which will replace the existing scale.
Warning message:
“Removed 10671 rows containing missing values or values outside the scale range (`geom_point()`).”
In [50]:
myeloid.genes
A data.frame: 1398 × 6
Gene_set_groupGene_setGeneExpression_directionCommon_nameSymbol.mouse
<chr><chr><chr><chr><chr><chr>
ImmuneTAMs PIK3CG Pik3cg
ImmuneTAMs ARG1 Arg1
ImmuneTAMs IL6 Il6
ImmuneTAMs IL10 Il10
ImmuneTAMs PDGFB Pdgfb
ImmuneTAMs TGFB1 Tgfb1
ImmuneTAMs CCL2 Ccl12
ImmuneTAMs CCL2 Ccl2
ImmuneTAMs IL23A Il23a
ImmuneTAMs IL17A Il17a
ImmuneTAMs CD163 Cd163
ImmuneTAMs MRC1 Mrc1
ImmuneTAMs MMP2 Mmp2
ImmuneTAMs MMP9 Mmp9
ImmuneTAMs VTCN1 Vtcn1
ImmunePan-Immune PTPRC CD45Ptprc
ImmuneGranulocyteFUT4 CD15Fut4
ImmuneGranulocyteCLC NA
ImmuneGranulocyteMS4A3 Ms4a3
ImmuneGranulocytePI3 NA
ImmuneGranulocyteTCN1 NA
ImmuneGranulocyteCPA3 Cpa3
ImmuneGranulocyteHDC Hdc
ImmuneGranulocyteGATA2 Gata2
ImmuneGranulocyteCXCL8 NA
ImmuneGranulocyteGATA1 Gata1
ImmuneGranulocyteMS4A2 Ms4a2
ImmuneGranulocyteSIGLEC8 Siglecf
ImmuneGranulocyteSIGLEC8 Siglece
ImmuneMast PLAU Plau
⋮⋮⋮⋮⋮⋮
Normal cellsOsteoblasts earlyDLX5 Dlx5
Normal cellsOsteoblasts earlySATB2 Satb2
Normal cellsOsteoblasts earlyALPL Alpl
Normal cellsOsteoblasts earlyCOL1A1 Col1a1
Normal cellsOsteoblasts earlyCOL1A2 Col1a2
Normal cellsOsteocyte PDPN1 NA
Normal cellsOsteocyte PHEX Phex
Normal cellsOsteocyte DMP1 Dmp1
Normal cellsOsteocyte CAPG Capg
Normal cellsOsteocyte MEPE Mepe
Normal cellsOsteocyte SOST Sost
Normal cellsChondrocyte SOX9 Sox9
Normal cellsChondrocyte COMP Comp
Normal cellsChondrocyte ACAN Acan
Normal cellsChondrocyte SOX5 Sox5
Normal cellsChondrocyte SOX6 Sox6
Normal cellsMesothelial cellsKRT19 Krt19
Normal cellsMesothelial cellsMSLN Msln
Normal cellsMesothelial cellsPDPN Pdpn
Tumor ccRCC_Tumor CA9 Car9
Tumor ccRCC_Tumor CD24 Cd24a
Tumor ccRCC_Tumor LTL NA
Tumor ccRCC_Tumor PAX2 Pax2
Tumor ccRCC_Tumor PAX8 Pax8
Tumor ccRCC_Tumor VEGFA Vegfa
Tumor ccRCC_Tumor COL23A1Col23a1
Tumor ccRCC_Tumor CP Cp
Tumor ccRCC_Tumor ENPP3 Enpp3
Tumor ccRCC_Tumor NDRG1 Ndrg1
Tumor ccRCC_Tumor SHISA9 Shisa9
In [14]:
options(repr.plot.width=15, repr.plot.height=7)
panc@meta.data %>% ggplot(aes(x = seurat_clusters, fill = predicted_doublet)) +
    geom_bar(stat = 'count', position = 'fill')
In [15]:
doublet.clusters <- panc@meta.data %>% 
    group_by(seurat_clusters, predicted_doublet) %>% 
    tally() %>% 
    group_by(seurat_clusters) %>%
    mutate(pct = n/sum(n)) %>% 
    filter(predicted_doublet, pct > 0.5) %>% 
    pull(seurat_clusters)

doublet.clusters
  1. 26
  2. 31
  3. 33
  4. 34
  5. 36
  6. 37
  7. 40
  8. 42
Levels:
  1. '0'
  2. '1'
  3. '2'
  4. '3'
  5. '4'
  6. '5'
  7. '6'
  8. '7'
  9. '8'
  10. '9'
  11. '10'
  12. '11'
  13. '12'
  14. '13'
  15. '14'
  16. '15'
  17. '16'
  18. '17'
  19. '18'
  20. '19'
  21. '20'
  22. '21'
  23. '22'
  24. '23'
  25. '24'
  26. '25'
  27. '26'
  28. '27'
  29. '28'
  30. '29'
  31. '30'
  32. '31'
  33. '32'
  34. '33'
  35. '34'
  36. '35'
  37. '36'
  38. '37'
  39. '38'
  40. '39'
  41. '40'
  42. '41'
  43. '42'
  44. '43'
  45. '44'
  46. '45'
  47. '46'
  48. '47'
  49. '48'
  50. '49'
In [20]:
liver.cell.types <- c('Hepatocytes' = '#2F8AC4',
  'Cholangiocytes' = '#52BCA3',
  'Doublet' = 'yellow',
  'Central venous LSECs' = '#ED645A',
  'Periportal LSECs' = '#E58606', 
  'Portal endothelial cells' = '#DAA51B',
   'LSECs' = '#ED645A',
  'Lymphatic endothelial cells'='#99C945',
  'Noninflammatory macs' = '#5D69B1',
                      'Kupffer cells' = '#5D69B1',
  'Inflammatory macs' = '#764E9F',
 
  
  'Portal fibroblasts' = '#CC61B0',
  'Hepatic stellate cells' = '#CC3A8E',
  
  'vSMCs' = '#24796C',
  'Pericytes' = '#0F8554',
  'Mesothelial cells' = '#A5AA99',
  'NK cells' = '#ede5cf',
  'T-cells' = '#6b705c',
  'B-cells' = '#e0c2a2',
  'Plasma' = '#d39c83',
  'Mast' = '#c1766f',
  'pDC' = '#a65461',
  'Monocytes' = '#813753',
  'cDC2' = '#541f3f',
   'DC' = '#541f3f',
  'mregDC'= '#8C785D')
In [ ]:

In [38]:
DefaultAssay(panc) = 'SCT'


panc@meta.data$cell_type_merged <- case_when(panc$predicted_doublet ~ 'Doublet',
                                   
                                     panc$seurat_clusters %in% doublet.clusters ~ 'Doublet',
                                     panc$seurat_clusters %in% c(26) ~ 'Doublet',
                                     panc$seurat_clusters %in% c(0,41, 14,17, 6,8,10,11, 20,21,27,2,9,19, 4,18,35, 28, 32) ~ 'Hepatocytes',
                                      panc$seurat_clusters %in% c(1,3) ~ 'Hepatic stellate cells',
                                             panc$seurat_clusters %in% c(38) ~ 'Pericytes',
                                      panc$seurat_clusters %in% c(5) ~ 'Kupffer cells',
                                             panc$seurat_clusters %in% c(23) ~ 'Inflammatory macs',
                                     panc$seurat_clusters %in% c(12,13,15) ~ 'Cholangiocytes',
                                             panc$seurat_clusters %in% c(7,16,22, 29) ~ 'LSECs',
                                             #panc$seurat_clusters %in% c(29) ~ 'Periportal LSECs',
                                             panc$seurat_clusters %in% c(24,43) ~ 'T-cells',
                                              panc$seurat_clusters %in% c(45) ~ 'Mesothelial cells',
                                              panc$seurat_clusters %in% c(30) ~ 'B-cells', # not plasma
                                             panc$seurat_clusters %in% c(25) ~ 'DC', #not B-cells
                                             panc$seurat_clusters %in% c(39) ~ 'cDC2', #not B-cells
                                             panc$seurat_clusters %in% c(32, 44) ~ 'Proliferating', 
                                             panc$seurat_clusters %in% c(46) ~ 'Adipocytes',
                                             panc$seurat_clusters %in% c(48) ~ 'Neurons',
                                             panc$seurat_clusters %in% c(47) ~ 'Skeletal muscle',
                                    TRUE ~ 'Unknown')
                                  


options(repr.plot.width=14, repr.plot.height=7)
DimPlot(object = panc,raster=T, group.by = 'cell_type_merged',cols = liver.cell.types,
        label=TRUE,label.size=4)
ggsave( "Dimplot_cell_type_merged.pdf",
       height=6,width=10,useDingbats=FALSE,limitsize = FALSE)

panc@meta.data %>% count(seurat_clusters, cell_type_merged) %>% filter(cell_type_merged=='Unknown')
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`

A data.frame: 1 × 3
seurat_clusterscell_type_mergedn
<fct><chr><int>
49Unknown71
In [ ]:

In [39]:
fwrite((panc@meta.data %>% select(cell_type_merged)), 
       '17_Mouse_Merged_normalized_SBR_2_10_26_weeks_02032025.tsv', sep = '\t', row.names = TRUE)
In [18]:
mouse.liver.cell.types <- c('Hepatocytes' = '#2F8AC4',
                            'Proliferating' = "#a9d6e5",
                            'Cholangiocytes' = '#52BCA3',
                            'Central venous LSECs' = '#e92e20',
                            'Mid lobular LSECs' = '#ED645A',
                            'Periportal LSECs' = '#E58606', 
                            'Portal endothelial cells' = '#DAA51B',
                            'Vascular endothelial cells' = '#ffb703',
                            'Lymphatic endothelial cells'='#99C945',
                            
                            'Kupffer cells' = '#5D69B1',
                            'Inflammatory macs' = '#764E9F',
                            'Portal fibroblasts' = '#CC61B0',
                            'Hepatic stellate cells' = '#CC3A8E',
                            'Tumor fibroblasts' = '#94346E',
                            'vSMCs' = '#24796C',
                            'Pericytes' = '#0F8554',
                            'Mesothelial cells' = '#A5AA99',
                            'NK cells' = '#ede5cf',
                            'T-cells' = '#6b705c',
                            'B-cells' = '#e0c2a2',
                            'Plasma' = '#d39c83',
                            'Mast' = '#c1766f',
                            'pDC' = '#a65461',
                            'Monocytes' = '#813753',
                            'Adipocytes' = '#DCB0F2',
                            'Skeletal muscle' = '#C9DB74',
                            'Neurons' = '#E73F74',
                            'Unknown' = 'grey',
                            'cDC2' = '#541f3f',
                            'DC'= '#8C785D')
In [3]:
setwd('/diskmnt/Projects/SenNet_analysis/Main.analysis/merged/merge_SBR_RNA_1/no_doublets')
In [2]:
obj <- readRDS('/diskmnt/Projects/SenNet_analysis/Main.analysis/merged/merge_SBR_RNA_1/no_doublets/17_Mouse_Merged_normalized_SBR_2_10_26_weeks_no_doublet.rds')

meta <- fread('/diskmnt/Projects/SenNet_analysis/Main.analysis/data_freeze/v1.1/metadata/17_Mouse_Merged_normalized_SBR_2_10_26_weeks_03052025.tsv', header = T) %>%
    column_to_rownames('V1')
In [5]:
head(meta)
A data.frame: 6 × 3
cell_type_broadcell_type_sencell_type_sen2
<chr><chr><chr>
SR006342_sbr_7448_Male_10_AAACCCAAGAAGCCAC-1Portal fibroblasts Portal fibroblasts Portal fibroblasts
SR006342_sbr_7448_Male_10_AAACCCAAGCCATTTG-1T-cells T-cells T-cells
SR006342_sbr_7448_Male_10_AAACCCACACGCGGTT-1Hepatic stellate cellsActivating HSCs Activating HSCs
SR006342_sbr_7448_Male_10_AAACCCACATTAAGCC-1Hepatocytes Hepatocytes Zone 1/2Hepatocytes
SR006342_sbr_7448_Male_10_AAACCCAGTATCCCAA-1Cholangiocytes Cholangiocytes Cholangiocytes
SR006342_sbr_7448_Male_10_AAACCCATCAGAATAG-1Cholangiocytes Cholangiocytes Cholangiocytes
In [6]:
obj <- AddMetaData(obj, meta)
In [19]:
options(repr.plot.width=15, repr.plot.height=10)

DimPlot(obj, group.by = 'cell_type_broad', cols = mouse.liver.cell.types, raster=T, label = TRUE) +
    coord_fixed()
ggsave('Dimplot_cell_type_broad.pdf', width = 13, height = 8, useDingbats = F)
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`

In [21]:
dim(obj)
  1. 25319
  2. 142491
In [ ]:

In [ ]:

In [ ]:

In [ ]: